AI Deployments in 2021: what has changed from 2020?
AI deployments have much more running for them than just money and resources. So, if an Al deployment fails, there is a massive amount of loss not just in terms of investment but lots of effort. According to Gartner, 80% of Artificial Intelligence-based deployments don’t reach their final stage, while 60% of successful deployments don’t make profits.
So, it is essential to ensure that the AI deployments should be successful and offer better ROI. Due to the pandemic, AI deployments have seen a massive impact. As the pandemic waves are more prudent, Artificial Intelligence has been the go-to technology to create intelligent solutions for tracing, tracking, and monitoring infected people.
However, there are challenges to AI deployments, even for COVID19 applications, that led to innovations. Such advancements will help AI deployments of 2021. So, let’s first discuss the challenges of AI deployments.
Top Challenges of AI deployments in 2021
#.1 AI as Business Case
Artificial Intelligence adoption, planning, development, and deployment is a massive undertaking that needs proper analysis. As a business, AI adoption and deployment must understand several use cases that will sync with organizational goals.
Take an example of the AI-based program called Argus from Deloitte that helps their teams find the tiniest discrepancies in any contract or documentation across several licenses. Mainly for enterprises dealing with multiple licenses, contracts, and several operational partnerships, an AI-based tool like Argus can be exemplary.
However, whether you need to invest such a massive amount of capital on an AI deployment for your operations needs careful consideration of use cases. However, what has changed is the way enterprises are looking at AI deployment as a business case in 2021 is an urgent need for automation of repetitive tasks.
The pandemic and urgency of remote capabilities have pushed enterprises planning for AI deployments to ramp up the pace. According to a McKinsey report, enterprises have moved 40X faster in technology adoption due to the pandemic, making it challenging to iterate better.
So, if you are dealing with a specific use case like marketing automation or even intelligent customer relationship management(CRM), you need to plan the entire AI deployment. Though the urgency due to pandemics may have made the AI adoption rapid, it has impacted the budget of deployments.
#2. AI Deployment Costs
AI deployment costs are a massive challenge amidst pandemics. Each AI project begins with the scoping, which leads to developing an algorithmic model, and finally deployment for operationalization. Thus, each stage of the AI lifecycle needs massive resources and financial backing.
Due to the massive economic downslide of markets during the pandemic, AI deployment budgets have seen an enormous impact. However, the benefits of AI outweigh the cost hurdles. A survey by Algorithmia suggests that more than 50% of respondents are looking to invest more in AI than last year.
For example, if you are a SaaS-based startup looking to leverage advanced AI-based technology for customer support, you can create custom virtual assistants. These intelligent assistants can help in reducing the customer response time and enhance the user experience.
So, rather than investing heavily in creating a centralized contact center for your customers, a SaaS-based startup can use virtual assistants or chatbots based on AI.
#3. Implementing Trustworthy AI
Misbehaving AI models and biased approaches by some Machine Learning algorithms have been problematic scenarios for enterprises. For example, as per a report by the Markup. Google Ads algorithm has a problem of suggesting gender-based keywords which look biased.
So, if you are looking at the deployment of trustworthy AI models, it is essential to check for misbehavior patterns or biased suggestions. Take an example of an AI use case of hyper-personalization.
An AI-based apparel firm called StitchFix uses an AI-based model for offering hyper personalizations of the apparel. Now, if the AI model at its core is biased, it will not be able to provide personalizations on such a high level as each person has a different need than others.
So, integrating a trustworthy AI solution becomes essential to your AI adoption plans. Another significant challenge in AI deployments is to ensure model scalability, resiliency, and governance.
#4. Scalability of AI deployments
Scaling your AI deployments can be a challenging task at hand. Let’s take an example of an AI tool for content generators. There are several such content generators in the market, and you need to have excellent scalability to offer an uninterrupted experience. In addition, there are several features that such an AI tool will need to deploy like, separate content categories, analytics, keyword identifiers, and others.
Such AI-based tools will be used by several marketers, and thus scalability is an essential part of your deployment strategy. However, scoping each feature, prototyping, iterating, and deploying will have several repetitive tasks, increasing resource consumption leading to higher costs. Here, you can leverage automation to reduce resource consumption and even optimize expenses.
For example, you can use AI-based business services like SAP to leverage AI-based data attribute recommendations and information classification. It uses historical data to train the model for data attribute recommendation, for which you will need to define a schema.
A data attribute recommendation will help you classify products, stores, keywords, and other classes through text input or numerical values. In addition, it enhances the data structure to offer missing information or unstructured data.
#5. Custom AI Models
Not every off-the-shelf AI model is suitable for your enterprise, and that is why customizations become vital. However, the challenge with customizations of models is the need for extensive historical data, computational resources, and skilled professionals within the organization. At its core, you need to train the AI model for customized functionality. However, you can use a hybrid approach by leveraging different AI models for several functionalities, which will otherwise need unique custom algorithms.
For example, you can customize your AI-based model for sales activities through different data patterns and algorithm training. A sales-based AI model may need different types of data like,
- Descriptive Data
- Activity Data
- Contextual Data
- Results Data
With each data type, you can customize models and enhance sales for your business. Though customization will need proper data structure and training of algorithms.
Going forward into 2021, AI deployments are getting far more challenging, and at the same time, some innovations offer efficient solutions. However, you need to consider several factors before you can choose a solution for your AI deployments.
Whether you want an off-the-shelf AI-based model or customize the models will affect the AI deployments. Off-the-shelf Ai models may be cost-efficient but restrict functionalities. However, for business-specific requirements, custom AI models seem to be a perfect choice.